This invention relates to apparatus and methods for automatically monitoring and evaluating manufacturing processes, for example operations which produce an ongoing stream of outputs such as discrete absorbent articles effective to absorb body fluids, for example disposable diapers. Such absorbent article products are typically fabricated as a sequence of work pieces being continuously processed on a continuous web and/or continuous processing line. Such absorbent article product generally comprises an absorbent core confined between a moisture impervious baffle of e.g. polyethylene and a moisture pervious body side liner of e.g. non-woven fibrous material. The absorbent articles are typically made by advancing one of the webs along a longitudinally extending path, applying the absorbent core to a first one of the webs, and then applying the second web over the combination of the first web and the absorbent core. Other elements such as elastics, leg cuffs, containment flaps, waste bands, and the like are added as desired for the particular product being manufactured, either before, during, or after, applying the second web. Such elements may be oriented longitudinally along the path, or transverse to the path, or may be orientation neutral.
Typical such manufacturing processes are designed to operate at steady state at a pre-determined set of operating conditions. A typical such process has a beginning and an end, and has a start-up period corresponding with the beginning of the operation of the process, and a shut-down period corresponding with the end of the operation of the process. The start-up period of the operation generally extends from the initiation of the process to the time the process reaches specified steady state conditions. The shut-down period of the operation generally extends from the time the process leaves steady state conditions to the termination of operation of the process.
While the process is operating at steady state conditions, the result desired from the process is desirably and typically achieved. For example, where the process is designed to produce a certain manufactured good, acceptable manufactured goods are normally produced when the process is operating at specified steady state conditions.
As used herein, "steady state" conditions represents more than a single specific set of process conditions. Namely, "steady state" represents a range of specified process conditions which correspond with a high probability that acceptable goods will be produced, namely that the products produced will correspond with specified product parameters.
Known statistical models and control models are based on assumptions that the goods produced during operation of a given such process represent a single homogeneous population of goods. The focus of such statistical models and control models is based on steady state conditions.
However, actual operation of many manufacturing processes, including highly automated processes, typically includes the occurrence of periodic, and in some cases numerous, destabilizing events. A "destabilizing event" is any event which upsets, interferes with, or otherwise destabilizes the ongoing steady state characteristics of either process parameters or unit-to-unit product parameters. A typical such destabilizing event is one which either causes unacceptable product to be made, or which causes the process controller to detect and/or report an anomalous condition, or both.
Depending on the nature and severity of any given destabilizing event, the destabilizing event may lead to any one or more of a number of possible results, for example, shutting down of the operation, speeding up or slowing down of the operation, changing one or more of the other operating parameters, sounding of an alarm to alert an operator, or the like. Upon the occurrence of such destabilizing events, the products fabricated by such manufacturing operation may be moving out of the tolerance range of predetermined required specifications whereupon corrective action should be taken in the manufacturing operation; or the product stream may move outside such specifications and should be culled from the product stream.
A variety of possible events in the manufacturing operation can cause the production of absorbent articles which fall outside the specification range. For example, stretchable materials can be stretched less than, or more than, desired. Elements can become misaligned relative to correct registration in the manufacturing operation. Timing between process steps, or speed of advance of an element, can be slightly out-of-tolerance. If such non-catastrophic changes in process conditions can be detected quickly enough, typically process corrections can be made, and the variances from target conditions can accordingly be reduced, without having to shut down the manufacturing operation and without having to cull, and thereby waste, product.
Other destabilizing events require more drastic action. Typical such more drastic destabilizing events are splices in any of the several inputs being fed into the process, web breaks, defective zones in an input material, the start-up period, the shut-down period, and the like. Typical responses to such more drastic anomalous destabilizing events might be culling product from the output, sending one or more corrective control commands to control actuators on the process line, sounding an alarm, slowing down the processing line, shutting down the process line, and the like.
A variety of automatic product inspection systems are available for routine ongoing automatic inspection of product being produced on a manufacturing line and for periodically and automatically taking samples for back-up manual evaluation. Indeed, periodic manual inspection of product samples is still important as a final assurance that quality product is being produced. The question to be addressed in that regard is directed toward timing and frequency of sampling and corresponding manual inspection and evaluation.
Where product is outside the specification range, and should be culled, it is desired to cull all defective product, but only that product which is in fact defective. If too little product is culled, or if the wrong product is culled, then defective product is inappropriately released into the stream of commerce. On the other hand, if product which in fact meets product specification is culled, then acceptable product is being wasted.
Body fluid absorbing absorbent articles such as are of interest herein for implementation of the invention are typically manufactured at speeds of about 50 to about 1200 articles per minute on a given manufacturing line. Accordingly, it is impossible for an operator to manually inspect each and every article so produced. If the operator reacts conservatively, culling product every time he/she has a suspicion, but no solid evidence, that some product may not meet specification, then a significant amount of in fact good product will have been culled. By contrast, if the operator takes action only when a defect has been confirmed using visual or other manual inspection, defective product may already have been released into the stream of commerce before the defective condition is discovered.
One way for the operator to inspect the product for conformity with the specification range is for the operator to periodically gather and inspect, off-line, physical samples of the product being produced. Random such inspections stand little prospect of detecting temporary out-of-specification conditions or of identifying leading and/or trailing elements of a group of defective products being produced. Where such samples are taken by an operator in response to a suspected out-of-specification condition, given the high rate of speed at which such articles are manufactured, by the time the operator completes the inspection, the suspected offensive condition may have existed long enough that questionable or defective product will have either been shipped or culled without the operator having any solid basis on which to make the ship/cull decision. Further, automated manufacturing process controls may have self-corrected the defect condition before the operator can complete the visual inspection and act on the results of such visual inspection.
While off-line inspection can be a primary determinant of quality, and typically defines the final quality and disposition of the product, on-line inspection, and off-line inspection of on-line-collected data, typically associated with certain manufacturing events, may provide valuable insight into both the operational characteristics of the manufacturing process, and the final quality parameters of the product.
Thus, in processes that operate at speeds such that manual inspection of each unit of product is impossible, the primary mechanism for inspecting each unit of product is an automatic inspection and control system, backed up by periodic manual inspections to confirm the accuracy of the decisions being made by the automatic inspection and control systems.
Known statistical control models can be used for, among other things, determining when samples are to be taken from the processing line, and manually analyzed and evaluated for conformance with pre-determined specifications for the product so being produced. Known such control models indicate sampling at periods based on steady state operation.
The desired contributions of a statistical control model are (i) to provide one or more sets of conditions under which product is automatically culled in anticipation that most if not all defective product will be so rejected and (ii) to provide one or more sets of conditions under which samples are taken for back-up manual inspection.
The problem with known control models is that they are designed for and focused on fine-tuning steady state processes; and do not take into full account the additional factors which come into play when significant destabilizing events occur. Specifically, known control models are optimized for bringing an operating system back to a set of target operating conditions when the operating system has strayed from the target set of conditions. Namely, known control models are optimized for maintaining steady state conditions. Correspondingly, known control models are not designed to distinguish product made during numerous temporary destabilizing events in terms of the probability that such product may represent a second or third product population. Neither are such control models optimized for sampling product as a direct result of when a destabilizing event occurs.
As a result, while existing statistical control models may be rather efficient at identifying and culling defective product resulting from random or unpredictable anomalous conditions in the process, when the process has a destabilizing event, known statistical control models tend to ignore the probability that the portion of the product population which is associated with the destabilizing event could contain a larger than average number of defective units. Namely, the product which is passed on for shipping may contain too many defective units, and the product which is rejected, culled, may contain an undesirably high fraction of good product which should have gone to shipping instead of being rejected or culled for disposal or recycling.
It is an object of the invention to provide methods for setting up and controlling a process, for example using a statistical control model, which provides for bringing the operating system to a set of target operating conditions assuming at least first and second product population segments, and corresponding first and second sets of assumptions about the respective product population segments, and the process steps to be taken to optimize process quality and efficiency, and corresponding product quality and efficiency.
It is another object to provide first and second control model segments, based on separate product population assumptions, or operating condition assumptions, for the respective first and second product population segments.
It is still another object to provide first and second separate and distinct sampling methods corresponding to the first and second product population segments.
It is a further object to provide first sampling methods for taking quality-check samples during steady state operation, and second different and distinct sampling methods for taking quality-check samples before, during, and after destabilizing events until such time as the process has been restored substantially to steady state conditions, or to conditions where acceptable product is being otherwise reliably produced.
It is yet another object to provide novel and improved statistical control model for controlling processes.
A still further object is to provide output populations, for example product populations, having improved levels of conformity with output specifications, without a significant increase in the amount of actually acceptable product which is in fact rejected by the automatic inspection and control system.
It is yet another object to provide product sampling and inspection methods which identify desirable changes to the automatic ship/cull decision settings of the automatic inspection, control, and cull system, which result in the automatic system better distinguishing actually acceptable output from actually defective output.